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Related Concept Videos

Diffusion01:12

Diffusion

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Diffusion is the passive movement of substances down their concentration gradients—requiring no expenditure of cellular energy. Substances, such as molecules or ions, diffuse from an area of high concentration to an area of low concentration in the cytosol or across membranes. Eventually, the concentration will even out, with the substance moving randomly but causing no net change in concentration. Such a state is called dynamic equilibrium, which is essential for maintaining overall...
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Diffusion01:21

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Diffusion is a type of passive transport. In passive transport, a substance tends to move from an area of high concentration to an area of low concentration until the concentration is equal across the space. For example, take the diffusion of substances through the air. When someone opens a perfume bottle in a room filled with people, the perfume is at its highest concentration in the bottle and is at its lowest at the edges of the room. The perfume vapor will diffuse, or spread away, from the...
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Facilitated Diffusion01:16

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The plasma membrane, a critical structure in cellular biology, houses an array of transporters, or carrier proteins, interspersed within its lipid bilayer. These proteins play a crucial role in solute transport through facilitated diffusion, a form of passive diffusion that uses transporters to move the molecules across the membrane.
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Behavior of Gas Molecules: Molecular Diffusion, Mean Free Path, and Effusion03:48

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Although gaseous molecules travel at tremendous speeds (hundreds of meters per second), they collide with other gaseous molecules and travel in many different directions before reaching the desired target. At room temperature, a gaseous molecule will experience billions of collisions per second. The mean free path is the average distance a molecule travels between collisions. The mean free path increases with decreasing pressure; in general, the mean free path for a gaseous molecule will be...
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Protein Diffusion in the Membrane01:24

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Proteins show rotational as well as lateral diffusion across the membrane. The lateral diffusion of proteins was confirmed through the cell fusion experiment where mouse and human cells were fused, resulting in hybrid cells. When the human and mouse cells fused, the specific membrane proteins on human and mouse cells were marked with the red and green-fluorescent markers, respectively. Initially, the red and green fluorescence was located on the respective hemisphere of the cell. As time...
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Assessment of Diffusion and Perfusion01:17

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Understanding and evaluating diffusion and perfusion is critical in assessing a patient's respiratory and circulatory health. These processes play key roles in maintaining the body's internal environment, ensuring that tissues receive adequate oxygen while waste products are efficiently removed.
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Measuring Connectivity in the Primary Visual Pathway in Human Albinism Using Diffusion Tensor Imaging and Tractography
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Mapping connectomes with diffusion MRI: deterministic or probabilistic tractography?

Tabinda Sarwar1, Kotagiri Ramamohanarao1, Andrew Zalesky2,3

  • 1School of Computing and Information Systems, The University of Melbourne, Parkville, Victoria, Australia.

Magnetic Resonance in Medicine
|October 11, 2018
PubMed
Summary
This summary is machine-generated.

Multi-fiber deterministic tractography is best for mapping human brain connectomes, offering superior accuracy. Probabilistic methods require thresholding to improve performance, but deterministic approaches remain optimal for whole-brain fiber tracking.

Keywords:
connectomediffusion MRIground truthnetworkphantomtractography

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Area of Science:

  • Neuroscience
  • Computational Biology
  • Medical Imaging

Background:

  • Human connectomics requires high-throughput reconstruction of white matter fiber bundles for whole-brain mapping.
  • Scaling tractography presents challenges like minimizing spurious connections and addressing gyral biases.

Purpose of the Study:

  • To compare deterministic and probabilistic tractography algorithms for their suitability in mapping human connectomes.
  • To identify the optimal tractography approach for accurate and efficient whole-brain connectome reconstruction.

Main Methods:

  • Development of numerical connectome phantoms with realistic network topologies, matched to in vivo diffusion MRI (dMRI) data.
  • Evaluation of tensor-based and multi-fiber deterministic and probabilistic tractography implementations using these phantoms.

Main Results:

  • Multi-fiber deterministic tractography achieved the highest accuracy (F-measure = 0.35) for connectome phantoms mirroring in vivo dMRI complexity.
  • Probabilistic algorithms showed lower specificity (F = 0.19) due to numerous false-positive connections.
  • Thresholding improved probabilistic algorithm performance (F = 0.38), but multi-fiber deterministic tractography remained superior (F = 0.42) with thresholding.

Conclusions:

  • Multi-fiber deterministic tractography is highly suitable for connectome mapping.
  • Connectome thresholding is a necessary step for optimizing probabilistic tractography algorithms.